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arXiv 2022年

作者： Soloperto, Raffaele Mesbah, Ali Allgöwer, Frank Institute for Systems Theory and Automatic Control University of Stuttgart Pfaffenwaldring 9 Stuttgart70569 Germany Department of Chemical and Biomolecular Engineering University of California BerkeleyCA94720 United States

In this paper, we propose a novel model predictive control (MPC) framework for output tracking that deals with partially unknown constraints. The MPC scheme optimizes over a learning and a backup trajectory. The learning trajectory aims to explore unknown and potentially unsafe areas, if and only if this might lead to a potential performance improvement. On the contrary, the backup trajectory lies in the known space, and is intended to ensure safety and convergence. The cost function for the learning trajectory is divided into a tracking and an offset cost, while the cost function for the backup trajectory is only marginally considered and only penalizes the offset cost. We show that the proposed MPC scheme is not only able to safely explore the unknown constraints, but also escape from local minima that may arise from the presence of obstacles. Moreover, we provide formal guarantees for convergence and recursive feasibility of the MPC scheme, as well as closed-loop constraint satisfaction. Finally, the proposed MPC scheme is demonstrated in simulations using an example of autonomous vehicle driving in a partially unknown environment where unknown obstacles are present. Copyright © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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Strategic Monitoring of Networked systems with Heterogeneous Security Levels

arXiv

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arXiv 2023年

作者： Milošević, Jezdimir Dahan, Mathieu Amin, Saurabh Sandberg, Henrik Electrical Engineering and Computer Science The University of Belgrade Belgrade Serbia Electrical Engineering and Computer Science The KTH Royal Institute of Technology Stockholm Sweden Computational Science and Engineering The Massachusetts Institute of Technology CambridgeMA United States Systems Engineering The University of California Berkeley BerkeleyCA United States Engineering Physics Lund University Lund Sweden Automatic Control Lund University Lund Sweden

We consider a strategic network monitoring problem involving the operator of a networked system and an attacker. The operator aims to randomize the placement of multiple protected sensors to monitor and protect components that are vulnerable to attacks. We account for the heterogeneity in the components’ security levels and formulate a large-scale maximin optimization problem. After analyzing its structure, we propose a three-step approach to approximately solve the problem. First, we solve a generalized covering set problem and run a combinatorial algorithm to compute an approximate solution. Then, we compute approximation bounds by solving a nonlinear set packing problem. To evaluate our solution approach, we implement two classical solution methods based on column generation and multiplicative weights updates, and test them on real-world water distribution and power systems. Our numerical analysis shows that our solution method outperforms the classical methods on large-scale networks, as it efficiently generates solutions that achieve a close to optimal performance and that are simple to implement in practice. © 2023, CC BY.

关键词： Game theory

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High Real-time Locomotion Recognition Algorithm Using Inertial Measurement Units

High Real-time Locomotion Recognition Algorithm Using Inerti...

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2022 CSAA/IET International Conference on Aircraft Utility systems, AUS 2022

作者： Wang, Jiaqi Wang, Xingjian Miao, Yinan Wang, Shaoping An, Mailing Miao, Yiqi Gavrilov, Alexander I. School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Qingdao Research Institute Beihang University Qingdao266104 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing100191 China Bauman Moscow State Technical University Department of Automatic Control Systems Moscow105005 Russia

ISBN: (数字)9781839537769

ISBN: (纸本)9781839537769

Human-machine interaction devices (HMI), such as assisted exoskeletons and assisted prostheses, that improve human motion behaviours are playing an increasingly important role in various fields. In order for these HMI devices to provide accurate and appropriate assistance to wearers, the real-time recognition of locomotion with accuracy is among the key issues to be solved. This paper proposes a high real-time motion pattern recognition algorithm using inertial measurement units (IMU) that can accurately and quickly identify the current motion state by collecting various motion parameters of the lower limb during human walking. The algorithm uses a trajectory solving and motion direction discrimination method based on the shank zero velocity point and a terrain classification method based on an F-score improved support vector machine (F-score-SVM). In this paper, experimental data was used to verify the performance of the algorithm. Results show that the algorithm performs better than traditional recognition algorithms in complex environments. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Support vector machines

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History Data Driven Distributed Consensus in Networks

arXiv

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arXiv 2022年

作者： Renganathan, Venkatraman Fontan, Angela Ganapathy, Karthik Department of Automatic Control LTH Lund University Lund Sweden Division of Decision and Control Systems KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical Engineering The University of Texas at Dallas RichardsonTX United States

The association of weights in a distributed consensus protocol quantify the trust that an agent has on its neighbors in a network. An important problem in such networked systems is the uncertainty in the estimation of trust between neighboring agents, coupled with the losses arising from mistakenly associating wrong amounts of trust with different neighboring agents. We introduce a probabilistic approach which uses the historical data collected in the network, to determine the level of trust between each agent. Specifically, using the finite history of the shared data between neighbors, we obtain a configuration which represents the confidence estimate of every neighboring agent’s trustworthiness. Finally, we propose a History-Data-Driven (HDD) distributed consensus protocol which translates the computed configuration data into weights to be used in the consensus update. The approach using the historical data in the context of a distributed consensus setting marks the novel contribution of our paper. Copyright © 2022, The Authors. All rights reserved.

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A Study for Obstacle Determination fusing MMW Radar and Vision Sensor 2

A Study for Obstacle Determination fusing MMW Radar and Visi...

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2nd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Rui, Wang Xin, Gu Tao, Wang National Key Laboratory of Science and Technology on Aerospace Intelligence Control Beijing Aerospace Automatic Control Institute Beijing China China Academy of Launch Vehicle Technology Department of Research Development Center Beijing China School of Intelligent Systems Engineering Sun Yat-Sen University Guangzhou China

ISBN: (纸本)9781665428767

This paper, on the basis of attention mechanism and driver's visual consciousness, presented a robust and adaptive approach for on-road obstacle boundary detection through fusing vision sensor and millimeter wave (MMW) radar in order to implement the navigation task, which is the sequent research work of radar and vision fusion for autonomous ground vehicle (AGV) navigation. It is the first most important issue for practically real-time precise guidance, navigation and control (GNC) of AGV. The novel exploration of prior knowledge about the features, shadow and edge or the position indicator lamps was discussed and employed with the attentive information from millimeter wave radar. An adaptive threshold algorithm based on the local significant peak and trough inside peaks determination (LSiPTiD) of the gray image histogram was provided in order to automatically judge the validity of obstacle shadow detection and detect the obstacle robustly. Vertical edge detection and possible intensity mutant were integrated with shadow and radar vision fusion point so as to recognize obstacle boundary correctly and robustly for further GNC task. In the end, through huge experiments of real on-road measured data, the algorithm was verified to be robust, adaptive and fast for real-time applications in natural road environment. © 2021 IEEE.

关键词： Behavioral research

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Towards Data-Driven Modelling of Sumoylation Following Heat Shock 20

Towards Data-Driven Modelling of Sumoylation Following Heat ...

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Proceedings of the 2020 7th International Conference on Biomedical and Bioinformatics engineering

作者： Manyu Zhang Yifei Zhang Alice Zhao Chun Guo Lingzhong Guo University of Sheffield UK The University of Sheffield and Department of Automatic Control and Systems Engineering UK The University of Sheffield and Department of Biomedical Science UK The University of Sheffield and Department of Automatic Control and Systems Engineering and INSIGNEO institute for In Silico Medicine UK

ISBN: (纸本)9781450388221

Understanding how cell fate is determined when exposed to extreme stresses such as heat shock is critical in biomedical systems. It has long been understood that exposure of cells to high temperature typically protect themselves with a heat shock response (HSR), where accumulation of denatured or unfolded proteins triggers the synthesis of heat shock proteins (HSPs) through the heat shock transcription factor, e.g., heat shock factor 1 (HSF1). Recent experimental work has also shown that protein posttranslational modifications (PTMs) such as SUMOylation play crucial roles in cellular responses to heat shock. As a complementary approach to the current experimental methodologies, in this study we aim to develop a mathematical model of SUMOylation-development synergism of HSR for the purpose of studying the dynamical behaviour of HSR quantitatively. The structure of our dynamical model is derived mostly from mass action kinetics while the model parameters are optimized by using a genetic algorithm (GA) based data-driven approach. The preliminary results show GA based data-driven approach has potentials for our modelling purpose.

关键词： quantitative refinement data-driven modelling parameter optimization Heat shock response (HSR) genetic algorithm (GA)

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Linearized ADMM for Nonsmooth Nonconvex Optimization with Nonlinear Equality Constraints

Linearized ADMM for Nonsmooth Nonconvex Optimization with No...

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IEEE Conference on Decision and control

作者： Lahcen El Bourkhissi Ion Necoara Panagiotis Patrinos Department of Automatic Control and Systems Engineering Universitatea Nationala de Stiinta si Tehnologie Politehnica Bucuresti Bucharest Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest Romania Department of Electrical Engineering (ESAT-STADIUS) KU Leuven Leuven Belgium

This paper proposes a new approach for solving a structured nonsmooth nonconvex optimization problem with nonlinear equality constraints, where both the objective function and constraints are 2-blocks separable. Our method is based on a 2-block linearized ADMM, where we linearize the smooth part of the cost function and the nonlinear term of the functional constraints in the augmented Lagrangian at each outer iteration. This results in simple subproblems, whose solutions are used to update the iterates of the 2 blocks variables. We prove global convergence for the sequence generated by our method to a stationary point of the original problem. To demonstrate its effectiveness, we apply our proposed algorithm as a solver for the nonlinear model predictive control problem of an inverted pendulum on a cart.

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Features of Space Vector Pulse Width Modulation in 5-Phases Cascaded High Voltage Converters

Features of Space Vector Pulse Width Modulation in 5-Phases ...

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Advanced Technology (KhPIWeek), IEEE KhPI Week on

作者： Victor Busher Oleksii Chornyi Valerii Tytiuk Oksana Glazeva Vitaliy Kuznetsov Dept. of Electrical Engineering and Electronics National University “Odessa Maritime Academy” Odessa Ukraine Institute of Electromechanics Energy Saving and Automatic Control Systems Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine Kryvyi Rih National University Kryvyi Rih Ukraine Dept. of the electrical engineering Ukrainian State University of Science and Technologies Dnipro Ukraine

ISBN: (数字)9798350384499

ISBN: (纸本)9798350384505

The purpose of this work is to analyze the capabilities of space-vector pulse-width modulation when controlling a 5-phase high-voltage cascade frequency converter in normal mode and several types of emergency situations. Taking into account the large number of controlled power transistors, an asynchronous modulation method is proposed, based on calculating the amplitude values of individual phases at each time based on the duty cycle of space vectors, which allows the control system to use independent switching in each phase. In emergency modes, when individual H-bridges are damaged, it is proposed to use the balanced space vectors method, which ensures the symmetry of the electromagnetic field in the event of a short circuit of both individual cascades and one or two phases of the inverter. Methodology. We use a mathematical and geometric interpretation of variable amplitude space vectors when projecting voltages onto a symmetrical 5-phase load. Results. Analysis of the 1st harmonic of phase voltages showed that the use of the space vector modulation method in the normal operating mode of a 5-phase converter makes it possible to increase the utilization factor of DC voltage sources by 23.1% (instead of 15.6% for a 3-phase converter). If individual H-bridges fail and the voltages in the load phases are balanced, the utilization factor may decrease, but in any case it will remain greater than 1. In this case, the higher harmonics, despite significant distortions in the shape of the phase voltages, are mutually compensated in the electromagnetic field of the load, as evidenced by absence of pulsations and shocks on the graphs of transient processes of the electromagnetic torque of the engine. Balancing of the electromagnetic field in the load is maintained even if one or two phases of the inverter are short-circuited. This significantly increases the survivability of the electric drive, which is especially important in critical mechanisms and technological processes,

关键词： Space vector pulse width modulation Frequency modulation Phase modulation High-voltage techniques Aerospace electronics control systems Harmonic analysis Vectors Inverters Electromagnetic fields

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Predicting EEG Responses to Attended Speech via Deep Neural Networks for Speech

Predicting EEG Responses to Attended Speech via Deep Neural ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Emina Alickovic Tobias Dorszewski Thomas U. Christiansen Kasper Eskelund Leonardo Gizzi Martin A. Skoglund Dorothea Wendt Eriksholm Research Centre Denmark Department of Electrical Engineering Division of Automatic Control Linköping University Linkoping Sweden Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Germany Oticon A/S Denmark Department of Health Technology Technical University of Denmark Denmark

Attending to the speech stream of interest in multi-talker environments can be a challenging task, particularly for listeners with hearing impairment. Research suggests that neural responses assessed with electroencephalography (EEG) are modulated by listener’s auditory attention, revealing selective neural tracking (NT) of the attended speech. NT methods mostly rely on hand-engineered acoustic and linguistic speech features to predict the neural response. Only recently, deep neural network (DNN) models without specific linguistic information have been used to extract speech features for NT, demonstrating that speech features in hierarchical DNN layers can predict neural responses throughout the auditory pathway. In this study, we go one step further to investigate the suitability of similar DNN models for speech to predict neural responses to competing speech observed in EEG. We recorded EEG data using a 64-channel acquisition system from 17 listeners with normal hearing instructed to attend to one of two competing talkers. Our data revealed that EEG responses are significantly better predicted by DNN-extracted speech features than by hand-engineered acoustic features. Furthermore, analysis of hierarchical DNN layers showed that early layers yielded the highest predictions. Moreover, we found a significant increase in auditory attention classification accuracies with the use of DNN-extracted speech features over the use of hand-engineered acoustic features. These findings open a new avenue for development of new NT measures to evaluate and further advance hearing technology.

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A Global to Local Double Embedding Method for Multi-person Pose Estimation 15th

A Global to Local Double Embedding Method for Multi-person P...

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Xu, Yiming Li, Jiaxin Ding, Yan Wei, Hua-Liang Yingcai Honors College University of Electronic Science and Technology of China Chengdu China Key Laboratory of Dynamics and Control of Flight Vehicle Ministry of Education School of Aerospace Engineering Beijing Institute of Technology Beijing100081 China Department of Automatic Control and Systems Engineering University of Sheffield SheffieldS1 3JD United Kingdom

ISBN: (纸本)9783030695408

Multi-person pose estimation is a fundamental and challenging problem to many computer vision tasks. Most existing methods can be broadly categorized into two classes: top-down and bottom-up methods. Both of the two types of methods involve two stages, namely, person detection and joints detection. Conventionally, the two stages are implemented separately without considering their interactions between them, and this may inevitably cause some issue intrinsically. In this paper, we present a novel method to simplify the pipeline by implementing person detection and joints detection simultaneously. We propose a Double Embedding (DE) method to complete the multi-person pose estimation task in a global-to-local way. DE consists of Global Embedding (GE) and Local Embedding (LE). GE encodes different person instances and processes information covering the whole image and LE encodes the local limbs information. GE functions for the person detection in top-down strategy while LE connects the rest joints sequentially which functions for joint grouping and information processing in A bottom-up strategy. Based on LE, we design the Mutual Refine Machine (MRM) to reduce the prediction difficulty in complex scenarios. MRM can effectively realize the information communicating between keypoints and further improve the accuracy. We achieve the competitive results on benchmarks MSCOCO, MPII and CrowdPose, demonstrating the effectiveness and generalization ability of our method. © 2021, Springer Nature Switzerland AG.

关键词： Embeddings

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